A digitally recorded image can be used to determine data in the image via an image processing. A frequent application is the determination of characteristics of objects which are contained in the image. Such characteristics can, for example, be the size, position, centroid, periphery or roundness of at least one object in a limited image region or in the entire image. Another application is to determine a maximum resolution of the recording arrangement based on a gray value image with the gray value image having been recorded by the recording arrangement. In order to carry out such analyses, it is advantageous when the objects are separated or segmented clearly from the background. This procedure requires the setting of a limit or of a threshold value so that each gray value image point, which exceeds this value or drops below this value, is set in a new image either as a white point or a black point. For such a threshold value method, the starting image is binarized, more specifically, precisely two segments in the form of a background and an object are formed.
In the state of the art, many methods are known to binarize a gray value image by assigning a suitable threshold value. An overview is presented, for example, in the article of P. K. Sahoo et al entitled “A Survey of Thresholding Techniques” published in Computer Vision, Graphics and Image Processing 41, pages 233 to 260 (1988). Some of the methods presented in this article have the disadvantage that a subjective evaluation by a user is required during an intermediate step so that no objective image evaluation is achieved. In methods, which determine a threshold value in a completely automated manner, there is one disadvantage that the results of a binarization, which is achieved via the different methods, depart greatly from each other as is shown in Table 1 of the above-mentioned article. In some methods, important image details are no longer present after binarization and, in other methods, details are added which were not originally recorded. A user would therefore have to have a very precise knowledge as to which method is best suitable for which type of gray value image. Furthermore, the user would have to decide in advance as to which image data are significant in order to select a suitable image processing. In order to obtain a good binarization result, subjective evaluations would be needed in advance of applying an image processing method. Since there is a very low probability that the correct method would be used, several methods have to be applied for reliability and the binarization results compared to each other. Such a procedure is associated with a high expenditure of time.